Burst Loss Reduction Using Fuzzy-Based Adaptive Burst Length Assembly Technique for Optical Burst Switched Networks

The optical burst switching (OBS) paradigm is perceived as an intermediate switching technology prior to the realization of an all-optical network. Burst assembly is the first process that takes place at the edge of an OBS network.  It is crucial to the performance of an OBS network because it greatly influences loss and delay on such networks.  Burst assembly is an important process while  burst loss ratio (BLR) and delay are important issues in OBS.  In this paper, an intelligent burst assembly algorithm called a Fuzzy-based Adaptive Length Burst Assembly (FALBA) algorithm that is based on fuzzy logic and tuning of fuzzy logic parameters is proposed for OBS network. FALBA was evaluated against itself and the fuzzy adaptive threshold (FAT) burst assembly algorithm using 12 configurations via simulation. The 12 configurations were derived from three rule sets (denoted 0,1,2), two defuzzification techniques (Centroid [C]and Largest of Maximum[L]) and two aggregation methods (Max[M] and Sum[S]) of fuzzy logic.  Simulation results have shown that FALBA0LM has the best BLR performance when compared to its other configurations and the FAT. However, with respect to delay, FAT only outperforms all configurations of FALBA at low loads (0.0-0.4) but the performance of FAT also decreases as the load (0.4-1.0) increases. Therefore, at high loads (0.4-1.0)  FALBA2CS has the best delay performance. Our results deduce that FALBA0LM can be use

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine

Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers

Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms

ABSTRACT Diabetes Mellitus (DM) embraces a group of metabolic diseases which main characteristic is the presence of high glucose levels in blood. It is one of the diseases with major social and health impact, both for its prevalence and also the consequences of the chronic complications that it implies. One of the research lines to improve the quality of life of people with diabetes is of technical focus. It involves several lines of research, including the development and improvement of devices to estimate "online" plasma glucose: continuous glucose monitoring systems (CGMS), both invasive and non-invasive. These devices estimate plasma glucose from sensor measurements from compartments alternative to blood. Current commercially available CGMS are minimally invasive and offer an estimation of plasma glucose from measurements in the interstitial fluid CGMS is a key component of the technical approach to build the artificial pancreas, aiming at closing the loop in combination with an insulin pump. Yet, the accuracy of current CGMS is still poor and it may partly depend on low performance of the implemented Calibration Algorithm (CA). In addition, the sensor-to-patient sensitivity is different between patients and also for the same patient in time. It is clear, then, that the development of new efficient calibration algorithms for CGMS is an interesting and challenging problem. The indirect measurement of plasma glucose through interstitial glucose is a main confounder of CGMS accuracy. Many components take part in the glucose transport dynamics. Indeed, physiology might suggest the existence of different local behaviors in the glucose transport process. For this reason, local modeling techniques may be the best option for the structure of the desired CA. Thus, similar input samples are represented by the same local model. The integration of all of them considering the input regions where they are valid is the final model of the whole data set. Clustering is tBarceló Rico, F. (2012). Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17173Palanci

Intelligent traction motor control techniques for hybrid and electric vehicles

This thesis presents the research undertaken by the author within the field of intelligent traction motor control for Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) applications. A robust Fuzzy Logic (FL) based traction motor field-orientated control scheme is developed which can control multiple motor topologies and HEV/EV powertrain architectures without the need for re-tuning. This control scheme can aid in the development of an HEV/EV and for continuous control of the traction motor/s in the final production vehicle. An overcurrent-tolerant traction motor sizing strategy is developed to gauge if a prospective motor’s torque and thermal characteristics can fulfil a vehicle’s target dynamic and electrical objectives during the early development stages of an HEV/EV. An industrial case study is presented. An on-line reduced switching multilevel inverter control scheme is investigated which increases the inverter’s efficiency while maintaining acceptable levels of output waveform harmonic distortion. A FL based vehicle stability control system is developed that improves the controllability and stability of an HEV/EV during an emergency braking manoeuvre. This system requires minimal vehicle parameters to be used within the control system, is insensitive to variable vehicle parameters and can be tuned to meet a vehicle’s target dynamic objectives

MPC-Bases energy mangement system for hybrid renewable energies

Los sistemas de suministro de energía están formados por un conjunto de subsistemas que pueden interconectarse a través de la disposición de actuadores. El proceso es un sistema dinámico híbrido multivariable que presenta varios modos de configuración necesarios para el funcionamiento diario. En esta tesis se propone un sistema de gestión de energía basado en teorías de control. La principal dificultad que presentan los sistemas de suministro está en su dinámica definida por un conjunto de ecuaciones diferenciales y expresiones lógicas, además del carácter variable de la energía producida por las fuentes renovables. Con el fin de satisfacer el suministro de energía, se considera el diseño de un controlador híbrido basado en las predicciones de energía estimadas a partir de modelos físicos y mediciones. El control predictivo (MPC) es elegido como la estrategia de control, ya que es capaz de manejar las variaciones en el suministro y demanda de energía.Departamento de Ingeniería de Sistemas y Automátic

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words